Biological control systems have long been studied as possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. In this paper, we present a model of cerebellar control of a muscle-actuated, two-link, planar arm. The model learns in a trial-and-error fashion to produce bursts of muscle activity that accurately bring the arm to a specified target. When the cerebellum fails to bring the arm to the target, an extra-cerebellar module performs low-quality corrective movement, from which the cerebellum may update its program. In learning to perform the task, the cerebellum constructs an implicit inverse model of the plant. This model uses a combination of delayed sensory signals and recently-generated motor commands to compute the new output motor signal.
|Original language||English (US)|
|Number of pages||7|
|Journal||Proceedings - IEEE International Conference on Robotics and Automation|
|State||Published - Jan 1 1997|
ASJC Scopus subject areas
- Control and Systems Engineering